(IFPRI) team on microfinance conducted a survey on MFIs in Asia, Africa, and Latin ..... MFIs' mission, their ... Appui au développement autonome ..... involved in conflicts (Algeria, Somalia, Angola, and Sudan) or countries that receive less.
FCND DP No.
114
FCND DISCUSSION PAPER NO. 114
DISTRIBUTION, GROWTH, AND PERFORMANCE OF MICROFINANCE INSTITUTIONS IN AFRICA, ASIA, AND LATIN AMERICA Cécile Lapenu and Manfred Zeller
Food Consumption and Nutrition Division International Food Policy Research Institute 2033 K Street, N.W. Washington, D.C. 20006 U.S.A. (202) 862–5600 Fax: (202) 467–4439
June 2001
FCND Discussion Papers contain preliminary material and research results, and are circulated prior to a full peer review in order to stimulate discussion and critical comment. It is expected that most Discussion Papers will eventually be published in some other form, and that their content may also be revised.
ii
ABSTRACT
How many microfinance institutions (MFIs) exist in the developing world? What are their current performances? In 1999, an International Food Policy Research Institute (IFPRI) team on microfinance conducted a survey on MFIs in Asia, Africa, and Latin America in order to offer a new in-depth analysis on the distribution and performances of MFIs at the international level. A systematic sampling has been adopted through the contacting of international NGOs and networks supporting various MFIs. The information has been complemented by a review of publications and technical manuals on microfinance. The database of MFIs from 85 developing countries shows 1,500 institutions (790 institutions worldwide plus 688 in Indonesia) supported by international organizations. They reach 54 million members, 44 million savers (voluntary and compulsory savings), and 23 million borrowers. The total volume of outstanding credit is $18 billion. The total savings volume is $12 billion, or 72 percent of the volume of the outstanding loans. MFIs have developed at least 46,000 branches and employ around 175,000 staff. The IFPRI database underlines the presence of a multitude of MFIs that, except in unstable countries, are widespread, with no forgotten regions. MFIs are very diverse in terms of lending technologies and legal status, which allows room for innovation, but they remain highly concentrated. The data are analyzed by type of MFIs and by geographic regions. The results presented give an overview of the current development of MFIs and offer a benchmark for comparisons.
iii
CONTENTS
Acknowledgments............................................................................................................... v 1. Introduction..................................................................................................................... 1 2. Methodology................................................................................................................... 2 Difficulties of an International Overview and Previous Experiences............................. 2 Nature of the Information ............................................................................................... 3 Source of information ................................................................................................. 3 Selection...................................................................................................................... 6 Limitations of the Data ............................................................................................... 8 3. Global Overview of MFIs in the Developing World ...................................................... 9 Volume of Activities....................................................................................................... 9 Average Performance of MFIs...................................................................................... 11 Size of the MFIs............................................................................................................ 13 Distribution of MFIs, by Country ................................................................................. 14 4. Role and Performance of MFIs, by Type of Technology and Legal Status ................. 15 Type of MFIs, by Technology ...................................................................................... 15 Type of MFIs, by Legal Status ..................................................................................... 22 5. Role and Performance of MFIs, by Location................................................................ 24 Rural and Urban MFIs .................................................................................................. 24 MFIs, by Continent ....................................................................................................... 27 6. Summary and Conclusions ........................................................................................... 31 References......................................................................................................................... 34
TABLES 1
Achievements of the main inventories.....................................................................3
2
List of international NGOs contacted ......................................................................5
iv
3
List of networks contacted .......................................................................................6
4
Overview of the volume of activities of MFIs in the developing world................10
5
Average performance of MFIs in the developing world........................................12
6
Distribution of MFIs, by number of members .......................................................13
7
Criteria of the typology of MFI structure ..............................................................16
8
Distribution of activities, by type of MFI (including Indonesia), in percent .........19
9
Distribution of activities, by type of MFI (excluding Indonesia), in percent ........20
10
Outreach, by type of MFI.......................................................................................21
11
Regulation of MFIs according to size in number of members (percent) ...............23
12
Volume of activities of MFIs, by geographic location (including Indonesia), in percent.............................................................................................25
13
Volume of activities of MFIs, by continent (including Indonesia)........................27
14
Total population and average per capita GNP, by continent .................................27
15
Volume of activities of MFIs, by continent (excluding Indonesia) .......................28
16
Average performance of MFIs, by continent .........................................................28
FIGURES 1
Staff productivity, by type of MFI.........................................................................20
2
Staff productivity, by location ...............................................................................26
3
Staff productivity, by continent .............................................................................29
4
Size of loans and deposits ......................................................................................30
v
ACKNOWLEDGMENTS
This research emanates from the multicountry research program on rural finance by the International Food Policy Research Institute (IFPRI). We thank Aliou Diagne and Manohar Sharma of IFPRI, Franz Heidhues of Hohenheim University, and an anonymous reviewer for their comments. The financial support of the German Federal Ministry for Economic Cooperation and Development and of the French Minister of Foreign Affairs is gratefully acknowledged. Finally, we thank the international and national MFI networks as well as MFI donors for providing data. The paper is excerpted from an unpublished report to the Federal Ministry for Economic Cooperation and Development, Germany.
Cécile Lapenu Comité d’Echange, de Réflexion et d’Information sur les Systèmes d’Epargne-crédit (CERISE), Paris Manfred Zeller University of Goettingen, Germany
1
1. INTRODUCTION
How many microfinance institutions (MFIs) are there in the developing world? Where are they located? How many households do they reach? How well do they do in terms of repayment and outreach? While there have been previous efforts to inventory MFIs and to look for commonalities in their development and performance, the answers to these questions are still not fully known. In 1999, the International Food Policy Research Institute (IFPRI) team on microfinance conducted a survey of MFIs in Asia, Africa, and Latin America (summarized in Section 1). This study builds on that work and offers further clarification of the world of MFIs by giving a detailed analysis of the distribution, growth, and performance of the MFIs supported by donor organizations and addressing some of the recurring questions on their roles. The questions are analyzed for all the institutions of the sample (Section 2), by type of institutions, i.e., lending technology and legal status (Section 3), and by geographic location, i.e., rural or urban and continent (Section 4). Issues are addressed at an aggregated level, which requires readers to consider the observations with caution. However, the results give benchmarks for the purpose of making comparisons and can help identify questions to be pursued through further research.
2
2. METHODOLOGY
DIFFICULTIES OF AN INTERNATIONAL OVERVIEW AND PREVIOUS EXPERIENCES Three major documents provide an overview of MFIs (see Table 1): the Sustainable Banking with the Poor Inventory, A Worldwide Inventory of Microfinance Institutions (1996), the Microcredit Summit Directory of Institutional Profiles (1998), and Calmeadow’s Microbanking Bulletin (July 1999). However, some limits exist in the information provided by these inventories. Other inventories exist, but only at regional or national levels. The PASMEC/BIT/BCEAO Database for West Africa (1998) or the Credit and Development Forum Statistics (1998) for Bangladesh offer interesting information to supplement a worldwide inventory of MFIs. Case studies offer more detailed data and analysis about some innovative or well-known MFIs. The Food and Agriculture Organization of the United Nations (FAO) recently launched a Web site called AgriBankStat1; however, the inventory focuses on licensed financial institutions and excludes intentionally unregulated financial institutions. The target group of this inventory does not focus on MFIs.
1
http://www.fao.org/waicent/faoinfo/agricult/ags/agsm/banks/invent.htm
3
Table 1: Achievements of the main inventories Main inventories Sustainable Banking with the Poor, 1996. A Worldwide Inventory of Microfinance Institutions
The Microcredit Summit Campaign, 1998. Directory of Institutional Profiles
MicroBanking Bulletin, July 1999. Issue No 3, Calmeadow
Contents
Main results
Limits
! 200 MFIs with minimum 1,000 clients and 3 years of experience MFIs classified by type (150 NGOs, 28 credit unions, 16 banks, 8 saving banks) and by region (Asia, Africa, Latin America) ! Information on outreach, loan portfolio, deposit mobilization, institutional age, gender and group-based lending ! 925 member institutions of the Microcredit Summit Council of Practitioners ! Raw information on MFIs’ mission, their institutional and client profiles, and a basic description of services offered ! 86 MFIs classified by region, scale, and target market ! Thanks to the quality of the financial data, analysis of the performances in terms of financial sustainability
! 14 million loans totaling US$7 billion ! 46 million savings accounts totaling US$19 billion ! Banks account for 68 percent of the loan volume, and saving banks hold 62 percent of the savings ! Results suggest that NGOs serve a specialized and presumably poorer clientele
! No definition of microfinance ! Fractional information for the initial sample of MFIs defined at the country level ! Risks on selfreported information ! Needs updating
! 12.6 million clients with a high proportion of poor households ! 72 percent (9.1 million) clients are reached by only 34 programs ! 76 percent of the clients are women
! Incomplete and biased selection of the MFI ! No classification by type of MFIs ! Risks of inflated self-reported information
! 46 percent of the sample financially self-sufficient ! 29 percent achieving above 65 percent financial selfsufficiency ! Age and size of the MFIs strongly correlate with the adjusted return on assets
! Small sample ! No classification by type of MFIs and clients
NATURE OF THE INFORMATION Source of Information Given the previous experience in compiling an inventory of MFIs, this paper attempts a systematic sampling of MFIs to arrive at a more representative view of the world of MFIs. Instead of compiling MFIs present at the country level, international
4
nongovernmental organizations (NGOs) (Table 2) and networks supporting various MFIs (Table 3) were contacted.2 By contacting Acción International, for example, the authors could collect information on all MFIs the organization supports. The international NGOs and networks were asked to send information concerning their activities in the field of microfinance: countries where they work; by country and project the type of MFIs promoted (e.g., solidarity groups, village banks, cooperatives, etc.) with a definition of each type of structure; area targeted (rural, urban, mixed); number of staff; number of clients (members, borrowers, savers); volume of savings and outstanding loans; average size of the loans; repayment rate; donors; and complementary services provided. Of the 42 international NGOs contacted, 28 (67 percent) responded (Table 2).3 In some cases, information from the NGOs that did not respond was obtained through other means, such as case studies or publications. Of the 24 networks contacted, 12 (50 percent) responded (Table 3). Though only half of them responded, the information provided a broad overview of MFIs by region or country. Most of the networks that did not answer are national networks with more limited coverage of institutions.
2
Source of information for the lists of NGOs and networks: Web sites of well-known NGOs and network, Microcredit Summit Directory of Institutional profiles, Pôle Microfinancement (http://www.cirad.fr/ mcredit/present.html), publications on case studies, IFPRI contacts.
3
Some NGOs replied, but as they had not compiled information on all their projects around the world, it was difficult for them to provide the requested information.
5
Table 2: List of international NGOs contacted Institution
Head office
Answer?
Acción International
USA
Y
Action for Enterprise
USA
Y
Adventist Development and Relief Agency International
USA
N
Agriculture Coop Development International/Voluntary Overseas Coop
USA
Y
Appui au développement autonome
Luxembourg
N
Associacione per la Partecipazione allo Sviluppo
Italy
N
Calmeadow
Canada
Y
Canadian Centre for International Studies and Cooperation
Canada
Y
Canadian Cooperative Association
Canada
N
Canadian Feed the Children
Canada
Y
CARE
USA
Y
Catholic Relief Service
USA
Y
Centre International du Crédit Mutuel
France
Y
Centre International de Développement et de Recherche
France
Y
Christian Aid
UK
N
Christian Children (‘s) Fund
USA
Y
Christian Reformed World Relief Committee
USA; Canada
Y
Development International Desjardins
Canada
Y
Ecumenical Church Loan Fund
Switzerland
Y
Foundation for International Community Assistance
USA
Y
Freedom from Hunger
USA
Y
Grameen Trust
Bangladesh
Y
Groupe de Recherche et d’Echange Technologiques
France
Y
Interdisciplinare Projekt Consult
Germany
N
Institut de Recherche et d’Application des Methodes de Developpement
France
Y
International Coalition on Women and Credit
USA
Y
Mennonite Economic Development Associates
Canada
Y
Opportunity International Network
USA
N
Oxford Committee for Famine Relief
UK
N
PACT
USA
N
Plan International
USA
Y
PlaNet Finance
France
Y
Save the Children
USA
Y
Stromme Foundation
Norway
N
TechnoServe
USA
Y
Trickle Up Program
USA
N
Winrock International
USA
N
Women’s Opportunity fund
USA
N
Women’s World Banking
USA
N
World Organization of Credit Unions
USA
Y
World Relief Corporation
USA
Y
World Vision
USA
Y
6
Table 3: List of networks contacted Institution
Head office
Answer?
Action Aid India Agency for Cooperation and Research in Development Banking with the Poor Network Bees Trust Cashpor Inc. Centre de Services aux Cooperatives Consortium Alafia Credit Development Forum Credit Union Promotion Committee Fed. Nac. de Apoio aos Peq. Empreendimentos Federacion Paraguaya de Microempresarios FINRURAL GOJ/GON Micro Enterprise Project Katalysis North/South Dev Partnership Khula Enterprise Finance Limited Microcredit NGO Network Pakistan Microenterprise Innovation Project Microfin-Afric National Microcredit Network of Congo Near East Foundation Programme d’Appui aux Structures Mutualistes ou Coop d’Epargne et de Credit Palli Karma Sahayak Foundation Pride Africa UNDP Pacific Reg. Equitable & Sust. Human Dev.
India UK Australia South Africa Philippines Rwanda Benin Bangladesh India Brazil Paraguay Bolivia Jamaica USA RSA Pakistan Salvador Senegal DRCongo Egypt West Africa Bangladesh Kenya Fiji
Y N Y N Y N Y Y N N N Y N Y N N N N N Y Y Y Y Y
The information collected through the international NGOs and networks has been complemented by a review of publications and technical manuals and in particular with previous work done to compile the information about MFIs.
Selection Geographically, the information concerns Africa, Asia, and Latin America. MFIs from Eastern Europe and the republics of the ex-USSR were not included because of the risk of collecting only very partial information. (MFIs and their supporting networks are
7
rather new, and often different from those in Asia, Africa, and Latin America.) MFIs from countries with per capita GDPs above $5,000 were also excluded.4 In terms of size, MFIs that have been included have at least 500 members and/or 100 borrowers when they have been founded before 1996. All MFIs founded from 1996 to December 1998 have been integrated, whatever their size. As the idea is to concentrate on microfinance, it was essential to fix a limit in terms of size of the financial services offered. Any limit can look rather arbitrary, and ideally it should vary between the different countries concerned. The authors decided an amount that can be substantial to support a family’s microenterprise, but that may appear insignificant for a bigger enterprise with a large amount of capital or many employees. In the sample, an average loan size of less than $1,000 was used as a somewhat crude cutoff point to distinguish microfinance from commercial loans.5 All of the selected MFIs receive some form of international support, either through funding, technical assistance, or information dissemination.6
4
The only exceptions are Argentina and Uruguay with per capita GDPs of $8,380 and $5,760, respectively, which have been kept so that the whole continent of Latin America could be analyzed.
5
Based on this, institutions such as PAME/AGETIP Senegal, Wages/CARE Togo, ADMIC Mexico, and Caja Social Colombia have been excluded due to their average loan sizes of $3,350, $2,800, $2,600, and $2,300, respectively.
6
In the case of Bangladesh, where the Credit Development Forum collected an impressive amount of data on microfinance NGOs, we kept the NGOs receiving at least 10 percent of their funding from international donors. In Indonesia, the local system of MFIs is impressive, with around 7,000 rural banks, some of which have been in operation since 1895 (Lapenu 1998). However, most institutions, such as the BKD (village banks), are locally owned and financed. We took into account the institutions that receive support from donors (ADB, USAID). These still number more than 680 institutions (or nearly 50 percent of the entire sample).
8
This mode of sampling underestimates local initiatives and national programs. It also underestimates national associations and foundations, informal systems, and agricultural or microenterprise cooperatives, all of which offer credit and saving services to their members. There were reasons for this choice, however. First, national implementations are more difficult to list exhaustively. Second, the aim of this synthesis is to offer an overview of the role of donors and the international community in the development of MFIs. Finally, except for the informal credit and saving associations or for some specific countries, microfinance development still remains a largely internationally-driven initiative.
Limitations of the Data Of course, the task of providing a worldwide inventory of microfinance is condemned to be partial, and many MFIs will always be missing. From the institutions listed in the database, there is also missing data. When average sizes of the loans are not provided, there is a risk of misclassifying institutions, i.e., some may offer loans that average over $1,000. Moreover, missing data on the number of clients or volume of credit and savings lead to underestimates of the volume of activity. However, the larger the sample, the more accurate the overall picture, and with a sample of more than 1,000 MFIs, we have minimized the limitations caused by missing information. As with every inventory, it will be necessary to update the information regularly. This will, of course, create the opportunity to further refine the data.
9
In terms of reliability of the information, most of the data was self-reported by the MFIs or the network they belong to. However, when the information comes from supporting institutions, we assume that the accuracy of the data was checked by the supporting institution. Given the difficulties of obtaining accurate and comparable information based on accounting data or level of poverty of the clients, no information has been recorded on costs, sustainability, or profile of the clients. The distinction between rural and urban areas comes from MFIs’ self-assessment rather than a strict definition. Finally, the years of the data may differ (50 percent are from 1998, 39 percent from 1997, 4 percent from 1996, and the remaining 7 percent from 1992 to 1995, and 1999) but they give a general overview of the volume of microfinance activity.
3. GLOBAL OVERVIEW OF MFIs IN THE DEVELOPING WORLD
VOLUME OF ACTIVITIES This database of MFIs7 from 85 developing countries shows 1,500 institutions (790 institutions worldwide plus 688 in Indonesia) supported by international organizations (Table 4). They reach 54 million members, 44 million savers, and 17
7
See Lapenu 2000.
10
Table 4: Overview of the volume of activities of MFIs in the developing world Number of observations Number of countries Number of MFI recorded in the sample Number of MFI with data Number of local branches Number of staff Number of borrowers Number of savers Number of members Volume of savings ($) Volume of outstanding loans ($)
770a 770 770 384 262 526 364 650 464 519
Total 85 1,468 1,366 45,572 81,020 16,684,442 43,929,072 54,050,639 12,269,966,267 17,452,192,521
Source: IFPRI surveys on worldwide MFIs, 1999. a
The unit of analysis of the database is the MFI classified by country. However, in few cases, the data is aggregated. 688 MFIs in Indonesia have been registered as three aggregate institutions only in the database: 27 NGOs, 252 ex-LDKP, and 409 rural banks. Around 20 MFIs have also been aggregated due to the availability of aggregated data only. 792 is the total number of rows in the database, including 22 countries with no MFI. When the number of observation is low, as for example for the number of staff, the aggregated value (of total staff) is certainly underestimated.
million borrowers8 in 85 countries. MFIs have developed 46,000 branches. The total volume of outstanding credit is $18 billion. The total savings volume is $13 billion, or 72 percent of the volume of the outstanding loans. This represents a notable volume of savings in view of the frequent critics against MFIs, which focus more on credit at the
8
Some corrections can be reasonably added to replace some missing values: • If the number of borrowers is missing while the number of members is available (cooperatives in 42 percent of the cases), we take the average of the cooperative model, i.e., when the data are available, 40 percent of the cooperative members on average have outstanding loans. Thus, we assume that for all member-based institutions, 40 percent of members have outstanding loans; the total gives then 23,542,955 borrowers. • If the number of staff is missing, we take the average productivity of staff in the sample (120 loans by employee) and replace the number of staff by the number of borrowers divided by the average productivity. It gives a total of 175,067 staff members.
11
expense of savings mobilization. Of course, if MFIs were to distribute loans from the mobilized savings, the current amount is still insufficient. If the figures are viewed from the perspective of the population of developing countries, the global outreach of microfinance can be summarized as follows: on average for developing countries,9 1.5 percent of the total population are MFI members. The volume of credit disbursed is around $5 per inhabitant and $3 per inhabitant are mobilized as savings.
AVERAGE PERFORMANCE OF MFIs Repayment rates, as reported in the questionnaires, appear quite high at 91 percent (Table 5). If weighted by the loan volume, the rate increases to 98 percent, implying that MFIs with larger loan volumes, i.e., larger MFIs, seem to have better repayment rates than smaller MFIs. On average, it seems that staff productivity in number of loans is relatively low, with 120 borrowers per employee, and a portfolio of $20,000 of credit and $10,000 of savings. By contrast, the figures for banks average 187 borrowers per employee, with $50,000 of credit and $16,000 of deposits. It is difficult to evaluate the depth of outreach of the MFIs at such an aggregated level. However, the available data include three proxy variables by which to assess the access by the poor to the financial services: percentage of women clients, average loan size, and average deposit size. The unweighted figure suggests a high outreach to women
9
Average for the whole sample aggregated by country and weighted by national population.
12
by MFIs (78 percent). However, this result must be qualified, as only the small institutions have a high percentage of women members. Thus, if the size of the MFI (in terms of number of members) is taken into account, the share of women is only 45 percent. One can say, nevertheless, that the presence of women is significant.
Table 5: Average performance of MFIs in the developing world Number of observations
Average value
REPAYMENT Repayment (unweighted, percent) Repayment (weighted by volume of credit, percent)
347 347
91 98
STAFF PRODUCTIVITY Number of loans per staff Volume of loans per staff ($) Volume of savings per staff ($)
256 254 256
121 19,197 9,849
OUTREACH Percentage of women (unweighted) Percentage of women (weighted by number of MFI members) Loan size ($) Deposit size ($) Loan as a percentage of per capita GDP Deposit as a percentage of per capita GDP (**)
487 487 376 272 367 269
78 45 268 99 62 18
Source: IFPRI surveys on worldwide MFIs, 1999.
On average, the MFIs offer services of very small size, suitable for poor people: loans average under $300, and deposits under $100, representing 60 percent and 20 percent, respectively, of the annual GDP per capita for the loans and savings accounts.
13
SIZE OF THE MFIs Forty-eight percent of MFIs have fewer than 2,500 members, almost three-fourths have fewer than 10,000 members, and only 7.5 percent have more than 100,000 members—an impressive world of tiny institutions (Table 6). This diversity is due to the fact that competition is imperfect; donors and governments subsidize institutions of various sizes (with small MFIs receiving relatively larger shares of subsidies in relation to their costs); MFIs operate in different market segments (different products and different clientele); and small MFIs entering new market segments such as rural areas or rural poor have higher start-up costs. The combination of these factors leads to a financial system with a multitude of institutional types. The diversity in terms of size observed in the sample of MFIs shows that it is difficult to determine what the optimal size for an MFI should be. In fact, the optimal size may largely depend on the local context, e.g., competitors, the MFI’s objectives, its age, approach, clientele, etc.
Table 6: Distribution of MFIs, by number of members Class of size 0–2,500 2,501–10,000 10,001–100,000 More than 100,000 Total (valid) Source: IFPRI surveys on worldwide MFIs, 1999.
Frequency 307 156 123 47 633
Percentage of total 48.5 24.6 19.4 7.5 100.0
14
The world of MFI is highly concentrated: MFIs with more than 300,000 members (19 institutions in the database) account for 44 million members, i.e., 3 percent of the MFIs serve more than 80 percent of the total number of members!10 This extreme concentration underscores the current difficulty to significantly and rapidly increase MFIs’ breadth of outreach. It will be necessary to support MFIs and to innovate so that they can reach a significant scale in terms of number of clients and volume of activity.
DISTRIBUTION OF MFIs, BY COUNTRY With at least 85 countries having MFIs, there is a wide distribution of various microfinance models, with Latin America and East Asia particularly well served. Among the large countries that do not have any MFIs with international support are countries involved in conflicts (Algeria, Somalia, Angola, and Sudan) or countries that receive less international support for political reasons (Cuba, North Korea, Iran, Iraq, and Libya). The same reasons apply for a number of countries that have very low outreach (Democratic Republic of Congo, Afghanistan, Myanmar, Pakistan, and Liberia reach less than 0.1 percent of the population). A minimum of political and economic stability is required for MFIs to develop. However, low outreach figures (less than 0.1 percent) are also observed in countries with high populations (China, India, Nigeria, Egypt). Latin America and East Asia are particularly active for microfinance. The “giants” in terms of absolute number of members reached are found in Asia: Indonesia,
10
The 19 MFIs serve 81.1 percent of the members in the database.
15
Bangladesh, Thailand, Viet Nam, Sri Lanka, and India. In Latin America, Colombia, Ecuador, Bolivia, Mexico, Uruguay, and Honduras account for the largest number of members. In Africa, Eastern and Southern Africa (Kenya, Uganda, Zimbabwe, and Zambia) are particularly dynamic as well as the CFA-franc zone (Mali, Benin, Burkina Faso, Ivory Coast, and Togo). The largest distribution of loans and mobilization of savings in terms of GNP are recorded in South East Asia (Thailand, Bangladesh, Viet Nam, and Indonesia), Latin America (Bolivia, Honduras, Panama, Jamaica, and Colombia) and East and West Africa (Kenya, Togo, Benin, Mali, and Burkina Faso).
4. ROLE AND PERFORMANCE OF MFIs, BY TYPE OF TECHNOLOGY AND LEGAL STATUS
TYPE OF MFIs, BY TECHNOLOGY The MFIs have been classified into five major types, according to the main technology they use to provide financial services (see Table 7): cooperatives, solidarity groups, village banks, individual contracts, and linkage models. Some MFIs combine different approaches, e.g., individual and solidarity group models. These have been classified as mixed.11
11
One-hundred-and-fifty institutions of unknown type have been excluded from Table 7.
16
Table 7: Criteria of the typology of MFI structure 1. Cooperative/ ”mutualist” model
2. Solidarity group (GB type)
3. Village banks
4. Linkage model
5. Individual contract
Nature of the New group New group local organization On average, 100- Center (5-6 200 members groups of 5-10 members each)
New group On average, 50100 members
Pre-existing group; variable sizes, from c. 20 to hundreds of members
Individual relationship
Ownership of equity
Member (equity shares)
Supporting agency (donor, state, NGO, private bodies)
Member
Member
Supporting agency (donor, state, NGO, bank, private bodies)
Rules/decisionmaking
Democratic (one person = one vote)
Supporting agency/partially; may be group members
Democratic (members)
Supporting Supporting agency/ members agency
Eligibility/ screening
Payment of membership; sometimes type of activity or social group Member savings
Accepted as a member of a group by peers, or supporting institution External loans and grants
Village member; sometimes, payment of membership
Member of a preexisting SHG; peers, bank, or NGO approval
Member savings; External loans; External loans external loans members savings
Relations: savings/credit
Focus on savings; credit mostly from savings
Focus on credit; mainly compulsory savings
Focus on savings; in principle, credit from savings
Saving first (but Focus on both just as collateral) credit and savings services
Structure
Pyramidal structure unions or federations/ local branches; bottom-up
Pyramidal structure, mostly top-down
Decentralized at the village level (linkage with formal bank possible)
Decentralized at the village level, linkage with closest bank branch
Centralized with rural/local branches
Main type of guarantee
Savings
Group pressure
Savings, social pressure
Savings, social pressure, NGO intermediation
Classical guarantees, individual creditworthiness
Daily operations
Salaried workers and elected members
Salaried workers
Elected members (self-managed); some may be remunerated
Salaried worker from the formal institution; may be NGO staff
Salaried workers
Main source of funding
Information on the client, guarantees provided
17
The largest MFIs are the cooperative and individual models, with a smaller number among the solidarity groups. The linkage system and the village banks remain small, most of which have fewer than 50,000 members. If the size of MFIs is analyzed by type, the results can be summarized as follows:
•
Cooperatives: Very few cooperatives have under 1,000 members (10 percent of the sample of cooperative MFIs); many have 10,000 to 200,000 members. In fact, most cooperatives were formed more than ten years ago, and unsuccessful ones have vanished.
•
Solidarity groups: 37 percent have fewer than 1,000 members; 93.7 percent have fewer than 50,000 members. It seems to be a difficult task for solidarity groups to grow to a large scale, which is probably due to their geographical location—50 percent are located in rural areas, and 40 percent are in Africa (IFPRI surveys 1999), where low population density and poor infrastructure may limit their development. All solidarity group MFIs with more than 300,000 members are in Asia: BAAC (Thailand); Grameen Bank, BRAC, PROSHIKA, ASA (Bangladesh); Friends of Women’s World Banking (India); Viet Nam Bank for the Poor; and P4K (Indonesia). Higher scales of operation can be achieved in densely populated areas, whereas lower scales tend to gain competitive advantage in areas with lower density. Finally, there is no justification for solidarity group systems if the population density is very low (mainly due to cost of staff and
18
transaction costs related to transport). In this case, village bank and linkage models that rely on endogenous and voluntary organization become more attractive. •
Village banks and linkage: None of the village banks has more than 25,000 members. Except for the Self-Help Development Foundation/CARE, Zimbabwe, with 300,000 members, no linkage system has more than 30,000 members. By definition, village banks and linkage models are local organizations that tend by nature to remain smaller scale, though they are linked to the formal banking network or their own federations.
•
Individual: Most MFIs have fewer than 30,000 members.12 Three institutions have more than 80,000 members: BRI-UD Indonesia (18 million), Viet Nam Banks for Agriculture and Rural Development (4 million), and CERUDEB Uganda (86,000). Due to management costs, individual lending is not well suited to countries or regions with low income and low population densities.
If Indonesian MFIs are included, the individual approach predominates in terms of number of MFIs (Table 8). Next are solidarity groups and cooperatives. Members are predominantly from MFIs with individual approach. Next are, at the same level, cooperatives and group methodologies. The solidarity groups have the largest number of
12
In Indonesia, the 1992 Banking Act limited the geographical reach of rural banks, restricting them until 1997 to subdistricts, each of which encompasses, on average, 10 villages. Proposed changes to the regulatory framework will promote consolidation of smaller rural banks in larger ones.
19
borrowers. Even if the number of borrowers from the cooperative system was underestimated due to a lack of data (see footnote 10, with data corrected based on assumptions), it reveals a very active policy of lending for solidarity groups. The cooperative model dominates for loans and savings volume (around 60 percent), followed by the solidarity groups. In fact, the Indonesian individual MFIs are very numerous but, except for the BRI, mostly represent very small institutions at the village level.
Table 8: Distribution of activities, by type of MFI (including Indonesia), in percent Cooperative Number of MFIs Number of borrowers Number of savers Number of members Volume of savings Volume of credit
11.9 9.9 31.2 26.9 60.5 59.9
Solidarity Village Individual Linkage Mixed group bank contract model approach 16.4 67.8 25.9 28 28.9 34.8
7 1.8 0.5 0.8 0.1 0.2
58.3 17.9 41.7 42.5 10.4 4.5
4 0.3 0 0.9 0 0
2.4 2.3 0.6 0.9 0.1 0.7
Total 100 100 100 100 100 100
Source: IFPRI surveys on worldwide MFIs, 1999.
If Indonesian MFIs are excluded from the sample, solidarity groups dominate in terms of number of MFIs and of borrowers (Table 9). The cooperatives are the most important source for loans and for savings mobilization. Village banks account for an important number of MFIs and of branches, and account for 12.5 percent of members, but they remain very small in terms of volume. The linkage model and the village banks have the highest staff productivity in terms of number of loans, as they delegate distribution and supervision of the loans to local groups (informal group or village committee) (Figure 1). For the other MFIs, one
20
employee, on average, serves 110–130 loans. For loan volume, the individual approach is clearly above average, compensating for low productivity in number by the large volume disbursed.
Table 9: Distribution of activities, by type of MFI (excluding Indonesia), in percent Solidarity Village Individual Linkage Mixed group bank contract model approach
Cooperative Number of MFIs Number of borrowers Number of savers Number of members Volume of savings Volume of credit
27.8 11.9 53.8 41.1 67.3 62.2
37.1 80.6 43.6 42.4 32.3 36.3
16.4 2.1 1 1.3 0.1 0.2
3.9 2.1 0.5 12.5 0.1 0.7
9.3 0.4 0.1 1.4 0 0
5.6 2.8 1.1 1.3 0.1 0.7
100 100 100 100 100 100
Source: IFPRI surveys on worldwide MFIs, 1999.
x mi
e ag nk li
di in
vi
ll
ag
e
vi
du
ba
al
nk
p ou gr
al
Volume of loans ($) Number of loans
Volume of savings ($)
Number of loans
800 700 600 500 400 300 200 100 0
tu mu
Volume ($)
Figure 1: Staff productivity, by type of MFI 50000 45000 40000 35000 30000 25000 20000 15000 10000 5000 0
Total
21
In terms of outreach, village banks, solidarity groups, and linkage models are the approaches that focus mostly on women clients (Table 10). Village banks offer the smallest volume of transactions. On the other extreme, individual contracts provide the largest average loan, both in absolute terms ($737) and as a percentage of the per capita GNP (173 percent). The individual approach is found to have both a low depth of outreach to women and to the poor in general.
Table 10: Outreach, by type of MFI OUTREACH
Cooperative
Solidarity Village group bank
Individual Linkage Mixed contract model approach
Average percentage of female (unweighted) 54.6
87.2
83.6
40.4
76.1
76.6
41.2
83.7
76.2
28.9
87.2
72.1
369
255
122
737
218
306
94
52
25
173
45
61
301
37
32
78
28
64
28
8
6
61
8
14
Average percentage of female (weighted by number of members) Average loan ($) Average loan as percentage of per capita GDP Average deposit ($) Average deposit as percentage of per capita GDP
Source: IFPRI surveys on worldwide MFIs, 1999.
The best results in terms of depth of outreach are achieved by the models that delegate part of the distribution and supervision of the loans to nonsalaried workers, which compensates for the low volume of transactions and perhaps also for additional constraints due, for example, to high illiteracy rates or the remoteness of clients.
22
If one was to combine the good side of the performance of the different type of institutions, one may rapidly face trade-offs between local, endogenous, and small-scale organization, and large, anonymous, well-staffed structures.
TYPE OF MFIs, BY LEGAL STATUS MFIs have been classified by legal status: they may be NGOs, cooperatives, registered banking institutions, government organizations (GO), or projects.13 In terms of performances, banks record the best staff productivity (187 loans for an amount of $50,000 per employee), but their results are low in terms of depth of outreach, with few women among their clients (40 percent) and high size of transaction (average loan of $425). Cooperatives also have a low depth of outreach (45 percent of women, average loan of $339) and high staff productivity (144 loans, $30,000). On the contrary, NGOs have a good depth of outreach (73 percent of women, average loan of $228), but low staff productivity (104 loans, $12,700). The worst results are recorded for government organizations, with very low productivity and depth of outreach. Table 11 shows that 91.5 percent of MFIs with more than 100,000 members are regulated, while the same is true for only 16 percent of MFIs with fewer than 20,000 members. There is a large number of unregulated NGOs, accounting for 61.4 percent of the sample. However, in terms of volume of activity, unregulated NGOs represent only a
13
One hundred institutions for which the status was unavailable are excluded from the tables.
23
tiny proportion of loans and savings volumes (less than 2 percent of the sample). More than 95 percent of the volume of savings goes through regulated institutions.
Table 11: Regulation of MFIs according to size in number of members (percent) 0-20,000 Regulated (cooperative, bank, government organization) Unregulated (NGO, project) Not available Number total
15.8 69.0 15.2 538
20-100,000 51.6 35.5 12.9 62
>100,000 91.5 8.5 0 47
Total 24.6 61.4 14.0 650
Source: IFPRI surveys on worldwide MFIs, 1999.
As savings mobilization from the public is one of the main reasons for regulation of MFIs, these observations can give a fresh insight on the debate over regulation of MFIs. Clearly, all MFIs cannot be treated equally, and a huge proportion of the small MFIs could not fall under a formal, banking-type, regulation. The largest MFIs, in particular those mobilizing important savings, must be regulated. For the smallest ones, however, it is highly unlikely that all could be transformed into banks or other formal financial institutions, nor would the regulatory authorities have the capacity to supervise all of them. However, the implementation of a regulatory framework in a country does not necessarily mean that unregulated MFIs should disappear. It may be important to accept that two kinds of MFIs can coexist:
24
•
larger MFIs that concentrate on financial services, in particular, mobilizing savings, and that are falling under specific national regulation. Thanks to their official recognition in the formal financial system, they may receive loans from the commercial banking sector to leverage their capital.
•
NGOs using microfinance tools as one among others to alleviate poverty. In spite of their “informality,” these NGOs also have a duty to adhere to minimal internal rules to work on a professional and efficient basis: insure a high rate of repayment, charge interest rates that allow them to recover part of the costs, define appropriate services for their clients, and to not compete unfairly with other MFIs. These NGOs, as they receive funding from donors and remain out of a strict regulatory framework, may have opportunities to test innovations that can be used by the larger MFIs or that may eventually enable growth to scale if the innovation proves successful in the market. On the other hand, this second type of MFI can benefit from the information on regulation and best practices implemented by the first type of MFIs to improve their performance and governance. A few of them may eventually grow to large scale.
5. ROLE AND PERFORMANCE OF MFIs, BY LOCATION
RURAL AND URBAN MFIs The information on geographic location is missing for 33 percent of MFIs. For the Indonesian cases, most work in a mixed environment. From the data available, we
25
observe that MFIs are predominantly working in both urban and rural areas, presumably to diversify their portfolio of liabilities and assets (Table 12). Only 19.5 percent of MFIs specialize in rural areas where the majority of the poor in the developing world live. In terms of number of members, the results are surprising, with a very low percentage of members served in the urban areas and very small part of the transactions.
Table 12: Volume of activities of MFIs, by geographic location (including Indonesia), in percent
Number of MFIs Number of members Volume of savings Volume of credit
Rural
Urban
Mixed
Total
19.5 59.9 39.8 38.1
7.4 1.9 0.4 1.5
73.1 38.1 59.8 60.5
100 100 100 100
Source: IFPRI surveys on worldwide MFIs, 1999.
There are several possible explanations. First, the biggest institutions such as the BRIUD, the BAAC, the Grameen Bank, BRAC, and the Agricultural Bank of Viet Nam work in rural or mixed areas and account for the majority of members. They operate in rural, densely populated areas mainly characterized by irrigated agriculture. MFIs with more than 500,000 members account for 46 million members, i.e., 85 percent of the total number of members and, with the exception of three for which data are missing, all work in rural or mixed areas. Second, it seems that MFIs that serve only urban areas remain rather small, due perhaps to a high level of competition with other banking institutions. In the database, the average number of members of urban institutions is 11,000, with a maximum of 162,000 members (Credit Unions Uganda). Finally, only a few MFIs
26
specialize in urban areas, and even those that do also seek to serve rural, or at least periurban, areas. As expected, staff productivity is higher in urban areas (these areas are more densely populated and there is the possibility of larger transactions) (Figure 2); however, conditions are more difficult for MFIs in mixed areas, with a lower number of loans by staff (perhaps due to the large size of the area in which to reach a diverse clientele). In terms of savings mobilization, MFIs in mixed areas are most productive. Because of their diversified portfolio of loans and savings, they may have smoother cash flows and may be able to offer a variety of savings products on competitive terms. The outreach to women is lowest in rural areas, as is the volume of loan transactions.
200 180 160 140 120 100 80 60 40 20 0
50000 45000 40000 35000 30000 25000 20000 15000 10000 5000 0 Rural
Number of loans
Urban Volume of loans ($)
Volume ($)
Number of loans
Figure 2: Staff productivity, by location
Mix Volume of savings
27
MFIs, BY CONTINENT Asia is the most developed continent in terms of volume of MFI activities, with 70 percent of the institutions, 77 percent of the members, 55 percent of the savings volume, and 65 percent of the loan volume (Table 13).
Table 13: Volume of activities of MFIs, by continent (including Indonesia) Latin America
Africa
Asia
9.0 12.9 40.5 32.5
21.8 9.9 5.0 2.6
69.2 77.2 54.5 64.9
Percentage of MFIs Percentage of members Percentage of savings Percentage of credit Source: IFPRI surveys on worldwide MFIs, 1999.
Considering the relative size of the Asian population (74.6 percent of the population), and excluding Indonesia, Africa compares well in terms of number of MFIs (45 percent) (Tables 14 and 15). Still, Asia retains the majority of the savings and loan volumes. The number of MFIs and the number of clients remain more modest in Latin America compared to Asia; however, they mobilize an impressive amount of savings and distribute a significant amount of loans.
Table 14: Total population and average per capita GNP, by continent
Total population (million) Percentage of total population Average per capita GNP ($) Source: Excell database (1998).
Latin America
Africa
Asia
426 11.1 2,673
551 14.3 748
2,870 74.6 1,194
28
Table 15: Volume of activities of MFIs, by continent (excluding Indonesia) Latin America Percentage of MFIs Percentage of members Average members per MFI (*1,000) Percentage of savings Average vol. of savings per MFI (millions $) Percentage of credit Average vol. of credit per MFI (millions $)
18.6 19.9 62 45.2 79 33.9 69
Africa
Asia
45.0 15.4 19 5.6 3 27 2
36.4 64.7 95 49.2 28 63.4 52
Source: IFPRI surveys on worldwide MFIs, 1999.
African MFIs have the lowest repayment rates (Table 16). On the other extreme, Asia benefits from good repayment rates even if, on average, it does not have the highest per capita GNP. In the case of Africa, other conditions may explain these results, such as the weak enforcement of laws, and exposure to individual and covariant risks.
Table 16: Average performance of MFIs, by continent Latin America REPAYMENT Repayment (unweighted, percent) Repayment (weighted by volume of loans, percent) STAFF PRODUCTIVITY Number of loans Volume of loans ($) Volume of savings ($) OUTREACH Average percentage of female (nonweighted) Average percentage of female (weighted by number of members) Average loan ($) Average loan as percentage of per capita GDP Average deposit ($) Average deposit as percentage of per capita GDP Source: IFPRI surveys on worldwide MFIs, 1999.
Africa
Asia
93.1 94.3
88.7 91.6
95.6 98.6
146 59,329 5,888
145 21,955 16,253
81 6,037 3,034
73.3 53.9 418 33 590 20
69.9 47.5 261 82 75 24
87.8 44.8 153 35 62 7
29
Asian productivity is very low, both in terms of number of clients and volume, compared to Africa and Latin America (Figure 3). This may be due to the lower cost of labor, compared to professional staff in Africa and Latin America. This is a great advantage for Asian MFIs and may explain Asia’s high repayment rates. Surprisingly, staff productivity in terms of number of clients is the same between Latin America and Africa, whereas the authors expected that, due to constraints of infrastructure and low population density, productivity in Africa would have been lowest. However, employees in Latin America have loan portfolios three times larger than their African counterparts. Staff productivity in Africa is good in terms of number of loans, but the higher rates of poverty among their clients lead to lower transaction volume.
160
70000
140
60000
120
50000
100
40000
80 30000
60 40
20000
20
10000
0
Volume ($)
Number of loans
Figure 3: Staff productivity, by continent
0 Latin America
Number of loans
Africa
Volume of loans ($)
Asia Volume of savings ($)
With unweighted results, Asia reaches significantly more women, but this is only the case for small institutions. When results are weighted by number of members, the best
30
results are in Latin America, with 54 percent female members, whereas African and Asian MFIs have fewer than 50 percent women as members. The largest transactions take place in Latin America, the smallest in Asia. Interestingly, in terms of percentage of per capita GDP, Africa has the largest transactions. If African MFIs wish to increase their depth of outreach, they would need to decrease the volume of transactions. In fact, the large volume of loans as a percentage of per capita GDP in Africa could be partly due to the predominance of cooperatives, which reach a wealthier population. In Asia, solidarity groups dominate, while village banks are largely represented in Latin America.
Figure 4: Size of loans and deposits Average size of loans and deposits 450 400 350 300 250 200 150 100 50 0
420
261
Latin America Africa Asia
256
153 82 33 A v.Loan ($)
35
75 62
16 24 7
A v.Loan A v.D eposit A v.D ep (% (% G D P pc) ($) G D P pc)
African and Latin American MFIs work mostly in mixed urban and rural environments (65 and 92 percent of the members, respectively), while Asian MFIs focus more on rural areas (75 percent of the members). In Africa and Latin America, the relatively low presence of MFIs in rural areas, even though the populations are
31
predominantly rural, implies that the rural depth of outreach is low. In particular, agricultural finance for smallholders remains underexploited.
6. SUMMARY AND CONCLUSIONS
MFIs provide extensive coverage of Asia, Africa, and Latin America, and have adopted a wide range of innovations to overcome various constraints. However, they require stable macroeconomic and political environments to develop. Unstable countries are still out of reach of the international world of microfinance. On the other extreme, Southeast Asia, Latin America, and East and West Africa receive most of the international support and account for the majority of the clients and the volumes involved in microfinance. On the whole, MFIs reach 54 million members, who have received $18 billion in loans and accumulated $13 billion in savings. With these figures, the Micro-Credit Summit objective to reach 100 million poor people by 2005 appears be achievable if one were to assume that most of the current MFI clients were “poor.” However, MFIs are highly concentrated in size (3 percent of the largest MFIs reach 80 percent of the members). If the stakeholders of the Micro-Credit Summit wish to achieve their goal, further client growth among the bigger MFIs should be necessary. This is because the many small MFIs will not contribute much to the total numbers even if they would double or triple their client numbers by 2005. However, it will be necessary to support the change of scale of small but efficient MFIs.
32
In terms of lending technologies, cooperatives are responsible for the largest proportion of the credit volume and savings transactions, while solidarity groups have a very active policy in terms of number of borrowers. The village bank and linkage models, thanks to the delegation of supervision to local voluntary staff, record higher staff productivity and achieve better depth of outreach than other MFIs. Surprisingly, there were relatively few urban-oriented MFIs, and those that did focus in urban areas tended to reach peri-urban and/or rural areas as well. In terms of regulation and legal status, more than 95 percent of the volume of microfinance transactions goes through regulated institutions (bank or cooperative) and although 60 percent of MFIs are still unregulated, they only account for less than 2 percent of the volume of savings mobilized and loans disbursed. By continent, Asia accounts for the largest volume of activity and employs the largest number of staff (thanks to low labor costs). This allows for close monitoring and supervision. Africa is very active in the field of microfinance. Many efforts have been made to improve staff productivity, but the continent still faces the constraints of poverty and illiteracy, both of which limit transaction volume. Moreover, loan sizes are already high when expressed as a percentage of per capita GNP, and increasing the size of loan transactions would endanger the depth of outreach. Rural Africa still has relatively lower outreach, which calls for continued efforts to improve rural and agricultural finance. Latin America is extensively covered by MFIs and records the largest volume per transaction. However, MFIs there work essentially in urban or mixed areas, and rural outreach remains low.
33
More households in developing countries as currently reached are likely to benefit from future growth of the MFI sector. To support future growth, it will be necessary to support MFIs in their efforts to find demand-oriented products to broaden their clientele and to innovate in cost-efficient service delivery systems, so that they can sustainably increase their scale in terms of number of clients, volume of activity, and relative poverty level of clients.
34
REFERENCES Christen, R. P. 1999. Bulletin Highlights, MicroBanking Bulletin, Issue No. 3, July, Calmeadow: 19–49. CDF (Credit and Development Forum). 1998. Credit and Development Forum Statistics, Volume VI. Dhaka, Bangladesh. Lapenu, C. 1998. Indonesia’s rural financial system: The role of the state and private institutions. Case Studies in Microfinance, Sustainable Banking with the Poor, Asia Series. World Bank, Washington, D.C. Lapenu, C. 2000. Volume 3: Multicountry synthesis report on institutional analysis. Report to the Federal Ministry for Economic Cooperation and Development (BMZ), Germany. International Food Policy Research Institute, Washington, D.C. Microcredit Summit Campaign (The). 1998. Directory of institutional profiles. Washington, D.C. PA-SMEC/BIT, BCEAO, 1998. Banques de données sur les systèmes financiers décentralisés de 7 pays de l’UMOA (Bénin, Burkina Faso, Côte d’Ivoire, Mali, Niger, Sénégal et Togo). PA-SMEC, projet BIT/BCEAO, Dakar, Sénégal/ Genève, Suisse, 8 volumes. World Bank. 1996. Sustainable banking with the poor: A worldwide inventory of microfinance institutions. Washington, D.C.
FCND DISCUSSION PAPERS 01
Agricultural Technology and Food Policy to Combat Iron Deficiency in Developing Countries, Howarth E. Bouis, August 1994
02
Determinants of Credit Rationing: A Study of Informal Lenders and Formal Credit Groups in Madagascar, Manfred Zeller, October 1994
03
The Extended Family and Intrahousehold Allocation: Inheritance and Investments in Children in the Rural Philippines, Agnes R. Quisumbing, March 1995
04
Market Development and Food Demand in Rural China, Jikun Huang and Scott Rozelle, June 1995
05
Gender Differences in Agricultural Productivity: A Survey of Empirical Evidence, Agnes R. Quisumbing, July 1995
06
Gender Differentials in Farm Productivity: Implications for Household Efficiency and Agricultural Policy, Harold Alderman, John Hoddinott, Lawrence Haddad, and Christopher Udry, August 1995
07
A Food Demand System Based on Demand for Characteristics: If There Is "Curvature" in the Slutsky Matrix, What Do the Curves Look Like and Why?, Howarth E. Bouis, December 1995
08
Measuring Food Insecurity: The Frequency and Severity of "Coping Strategies," Daniel G. Maxwell, December 1995
09
Gender and Poverty: New Evidence from 10 Developing Countries, Agnes R. Quisumbing, Lawrence Haddad, and Christine Peña, December 1995
10
Women's Economic Advancement Through Agricultural Change: A Review of Donor Experience, Christine Peña, Patrick Webb, and Lawrence Haddad, February 1996
11
Rural Financial Policies for Food Security of the Poor: Methodologies for a Multicountry Research Project, Manfred Zeller, Akhter Ahmed, Suresh Babu, Sumiter Broca, Aliou Diagne, and Manohar Sharma, April 1996
12
Child Development: Vulnerability and Resilience, Patrice L. Engle, Sarah Castle, and Purnima Menon, April 1996
13
Determinants of Repayment Performance in Credit Groups: The Role of Program Design, Intra-Group Risk Pooling, and Social Cohesion in Madagascar, Manfred Zeller, May 1996
14
Demand for High-Value Secondary Crops in Developing Countries: The Case of Potatoes in Bangladesh and Pakistan, Howarth E. Bouis and Gregory Scott, May 1996
15
Repayment Performance in Group-Based credit Programs in Bangladesh: An Empirical Analysis, Manohar Sharma and Manfred Zeller, July 1996
16
How Can Safety Nets Do More with Less? General Issues with Some Evidence from Southern Africa, Lawrence Haddad and Manfred Zeller, July 1996
17
Remittances, Income Distribution, and Rural Asset Accumulation, Richard H. Adams, Jr., August 1996
18
Care and Nutrition: Concepts and Measurement, Patrice L. Engle, Purnima Menon, and Lawrence Haddad, August 1996
19
Food Security and Nutrition Implications of Intrahousehold Bias: A Review of Literature, Lawrence Haddad, Christine Peña, Chizuru Nishida, Agnes Quisumbing, and Alison Slack, September 1996
20
Macroeconomic Crises and Poverty Monitoring: A Case Study for India, Gaurav Datt and Martin Ravallion, November 1996
21
Livestock Income, Male/Female Animals, and Inequality in Rural Pakistan, Richard H. Adams, Jr., November 1996
22
Alternative Approaches to Locating the Food Insecure: Qualitative and Quantitative Evidence from South India, Kimberly Chung, Lawrence Haddad, Jayashree Ramakrishna, and Frank Riely, January 1997
FCND DISCUSSION PAPERS 23
Better Rich, or Better There? Grandparent Wealth, Coresidence, and Intrahousehold Allocation, Agnes R. Quisumbing, January 1997
24
Child Care Practices Associated with Positive and Negative Nutritional Outcomes for Children in Bangladesh: A Descriptive Analysis, Shubh K. Kumar Range, Ruchira Naved, and Saroj Bhattarai, February 1997
25
Water, Health, and Income: A Review, John Hoddinott, February 1997
26
Why Have Some Indian States Performed Better Than Others at Reducing Rural Poverty?, Gaurav Datt and Martin Ravallion, March 1997
27
"Bargaining" and Gender Relations: Within and Beyond the Household, Bina Agarwal, March 1997
28
Developing a Research and Action Agenda for Examining Urbanization and Caregiving: Examples from Southern and Eastern Africa, Patrice L. Engle, Purnima Menon, James L. Garrett, and Alison Slack, April 1997
29
Gender, Property Rights, and Natural Resources, Ruth Meinzen-Dick, Lynn R. Brown, Hilary Sims Feldstein, and Agnes R. Quisumbing, May 1997
30
Plant Breeding: A Long-Term Strategy for the Control of Zinc Deficiency in Vulnerable Populations, Marie T. Ruel and Howarth E. Bouis, July 1997
31
Is There an Intrahousehold 'Flypaper Effect'? Evidence from a School Feeding Program, Hanan Jacoby, August 1997
32
The Determinants of Demand for Micronutrients: An Analysis of Rural Households in Bangladesh, Howarth E. Bouis and Mary Jane G. Novenario-Reese, August 1997
33
Human Milk—An Invisible Food Resource, Anne Hatløy and Arne Oshaug, August 1997
34
The Impact of Changes in Common Property Resource Management on Intrahousehold Allocation, Philip Maggs and John Hoddinott, September 1997
35
Market Access by Smallholder Farmers in Malawi: Implications for Technology Adoption, Agricultural Productivity, and Crop Income, Manfred Zeller, Aliou Diagne, and Charles Mataya, September 1997
36
The GAPVU Cash Transfer Program in Mozambique: An assessment, Gaurav Datt, Ellen Payongayong, James L. Garrett, and Marie Ruel, October 1997
37
Why Do Migrants Remit? An Analysis for the Dominican Sierra, Bénédicte de la Brière, Alain de Janvry, Sylvie Lambert, and Elisabeth Sadoulet, October 1997
38
Systematic Client Consultation in Development: The Case of Food Policy Research in Ghana, India, Kenya, and Mali, Suresh Chandra Babu, Lynn R. Brown, and Bonnie McClafferty, November 1997
39
Whose Education Matters in the Determination of Household Income: Evidence from a Developing Country, Dean Jolliffe, November 1997
40
Can Qualitative and Quantitative Methods Serve Complementary Purposes for Policy Research? Evidence from Accra, Dan Maxwell, January 1998
41
The Political Economy of Urban Food Security in Sub-Saharan Africa, Dan Maxwell, February 1998
42
Farm Productivity and Rural Poverty in India, Gaurav Datt and Martin Ravallion, March 1998
43
How Reliable Are Group Informant Ratings? A Test of Food Security Rating in Honduras, Gilles Bergeron, Saul Sutkover Morris, and Juan Manuel Medina Banegas, April 1998
44
Can FAO's Measure of Chronic Undernourishment Be Strengthened?, Lisa C. Smith, with a Response by Logan Naiken, May 1998
45
Does Urban Agriculture Help Prevent Malnutrition? Evidence from Kampala, Daniel Maxwell, Carol Levin, and Joanne Csete, June 1998
46
Impact of Access to Credit on Income and Food Security in Malawi, Aliou Diagne, July 1998
FCND DISCUSSION PAPERS 47
Poverty in India and Indian States: An Update, Gaurav Datt, July 1998
48
Human Capital, Productivity, and Labor Allocation in Rural Pakistan, Marcel Fafchamps and Agnes R. Quisumbing, July 1998
49
A Profile of Poverty in Egypt: 1997, Gaurav Datt, Dean Jolliffe, and Manohar Sharma, August 1998.
50
Computational Tools for Poverty Measurement and Analysis, Gaurav Datt, October 1998
51
Urban Challenges to Food and Nutrition Security: A Review of Food Security, Health, and Caregiving in the Cities, Marie T. Ruel, James L. Garrett, Saul S. Morris, Daniel Maxwell, Arne Oshaug, Patrice Engle, Purnima Menon, Alison Slack, and Lawrence Haddad, October 1998
52
Testing Nash Bargaining Household Models With Time-Series Data, John Hoddinott and Christopher Adam, November 1998
53
Agricultural Wages and Food Prices in Egypt: A Governorate-Level Analysis for 1976-1993, Gaurav Datt and Jennifer Olmsted, November 1998
54
Endogeneity of Schooling in the Wage Function: Evidence from the Rural Philippines, John Maluccio, November 1998
55
Efficiency in Intrahousehold Resource Allocation, Marcel Fafchamps, December 1998
56
How Does the Human Rights Perspective Help to Shape the Food and Nutrition Policy Research Agenda?, Lawrence Haddad and Arne Oshaug, February 1999
57
The Structure of Wages During the Economic Transition in Romania, Emmanuel Skoufias, February 1999
58
Women's Land Rights in the Transition to Individualized Ownership: Implications for the Management of Tree Resources in Western Ghana, Agnes Quisumbing, Ellen Payongayong, J. B. Aidoo, and Keijiro Otsuka, February 1999
59
Placement and Outreach of Group-Based Credit Organizations: The Cases of ASA, BRAC, and PROSHIKA in Bangladesh, Manohar Sharma and Manfred Zeller, March 1999
60
Explaining Child Malnutrition in Developing Countries: A Cross-Country Analysis, Lisa C. Smith and Lawrence Haddad, April 1999
61
Does Geographic Targeting of Nutrition Interventions Make Sense in Cities? Evidence from Abidjan and Accra, Saul S. Morris, Carol Levin, Margaret Armar-Klemesu, Daniel Maxwell, and Marie T. Ruel, April 1999
62
Good Care Practices Can Mitigate the Negative Effects of Poverty and Low Maternal Schooling on Children's Nutritional Status: Evidence from Accra, Marie T. Ruel, Carol E. Levin, Margaret ArmarKlemesu, Daniel Maxwell, and Saul S. Morris, April 1999
63
Are Urban Poverty and Undernutrition Growing? Some Newly Assembled Evidence, Lawrence Haddad, Marie T. Ruel, and James L. Garrett, April 1999
64
Some Urban Facts of Life: Implications for Research and Policy, Marie T. Ruel, Lawrence Haddad, and James L. Garrett, April 1999
65
Are Determinants of Rural and Urban Food Security and Nutritional Status Different? Some Insights from Mozambique, James L. Garrett and Marie T. Ruel, April 1999
66
Working Women in an Urban Setting: Traders, Vendors, and Food Security in Accra, Carol E. Levin, Daniel G. Maxwell, Margaret Armar-Klemesu, Marie T. Ruel, Saul S. Morris, and Clement Ahiadeke, April 1999
67
Determinants of Household Access to and Participation in Formal and Informal Credit Markets in Malawi, Aliou Diagne, April 1999
68
Early Childhood Nutrition and Academic Achievement: A Longitudinal Analysis, Paul Glewwe, Hanan Jacoby, and Elizabeth King, May 1999
FCND DISCUSSION PAPERS 69
Supply Response of West African Agricultural Households: Implications of Intrahousehold Preference Heterogeneity, Lisa C. Smith and Jean-Paul Chavas, July 1999
70
Child Health Care Demand in a Developing Country: Unconditional Estimates from the Philippines, Kelly Hallman, August 1999
71
Social Capital and Income Generation in South Africa, 1993-98, John Maluccio, Lawrence Haddad, and Julian May, September 1999
72
Validity of Rapid Estimates of Household Wealth and Income for Health Surveys in Rural Africa, Saul S. Morris, Calogero Carletto, John Hoddinott, and Luc J. M. Christiaensen, October 1999
73
Social Roles, Human Capital, and the Intrahousehold Division of Labor: Evidence from Pakistan, Marcel Fafchamps and Agnes R. Quisumbing, October 1999
74
Can Cash Transfer Programs Work in Resource-Poor Countries? The Experience in Mozambique, Jan W. Low, James L. Garrett, and Vitória Ginja, October 1999
75
Determinants of Poverty in Egypt, 1997, Gaurav Datt and Dean Jolliffe, October 1999
76
Raising Primary School Enrolment in Developing Countries: The Relative Importance of Supply and Demand, Sudhanshu Handa, November 1999
77
The Political Economy of Food Subsidy Reform in Egypt, Tammi Gutner, November 1999.
78
Determinants of Poverty in Mozambique: 1996-97, Gaurav Datt, Kenneth Simler, Sanjukta Mukherjee, and Gabriel Dava, January 2000
79
Adult Health in the Time of Drought, John Hoddinott and Bill Kinsey, January 2000
80
Nontraditional Crops and Land Accumulation Among Guatemalan Smallholders: Is the Impact Sustainable? Calogero Carletto, February 2000
81
The Constraints to Good Child Care Practices in Accra: Implications for Programs, Margaret ArmarKlemesu, Marie T. Ruel, Daniel G. Maxwell, Carol E. Levin, and Saul S. Morris, February 2000
82
Pathways of Rural Development in Madagascar: An Empirical Investigation of the Critical Triangle of Environmental Sustainability, Economic Growth, and Poverty Alleviation, Manfred Zeller, Cécile Lapenu, Bart Minten, Eliane Ralison, Désiré Randrianaivo, and Claude Randrianarisoa, March 2000
83
Quality or Quantity? The Supply-Side Determinants of Primary Schooling in Rural Mozambique, Sudhanshu Handa and Kenneth R. Simler, March 2000
84
Intrahousehold Allocation and Gender Relations: New Empirical Evidence from Four Developing Countries, Agnes R. Quisumbing and John A. Maluccio, April 2000
85
Intrahousehold Impact of Transfer of Modern Agricultural Technology: A Gender Perspective, Ruchira Tabassum Naved, April 2000
86
Women’s Assets and Intrahousehold Allocation in Rural Bangladesh: Testing Measures of Bargaining Power, Agnes R. Quisumbing and Bénédicte de la Brière, April 2000
87
Changes in Intrahousehold Labor Allocation to Environmental Goods Collection: A Case Study from Rural Nepal, Priscilla A. Cooke, May 2000
88
The Determinants of Employment Status in Egypt, Ragui Assaad, Fatma El-Hamidi, and Akhter U. Ahmed, June 2000
89
The Role of the State in Promoting Microfinance Institutions, Cécile Lapenu, June 2000
90
Empirical Measurements of Households’ Access to Credit and Credit Constraints in Developing Countries: Methodological Issues and Evidence, Aliou Diagne, Manfred Zeller, and Manohar Sharma, July 2000
91
Comparing Village Characteristics Derived From Rapid Appraisals and Household Surveys: A Tale From Northern Mali, Luc Christiaensen, John Hoddinott, and Gilles Bergeron, July 2000
FCND DISCUSSION PAPERS 92
Assessing the Potential for Food-Based Strategies to Reduce Vitamin A and Iron Deficiencies: A Review of Recent Evidence, Marie T. Ruel and Carol E. Levin, July 2000
93
Mother-Father Resource Control, Marriage Payments, and Girl-Boy Health in Rural Bangladesh, Kelly K. Hallman, September 2000
94
Targeting Urban Malnutrition: A Multicity Analysis of the Spatial Distribution of Childhood Nutritional Status, Saul Sutkover Morris, September 2000
95
Attrition in the Kwazulu Natal Income Dynamics Study 1993-1998, John Maluccio, October 2000
96
Attrition in Longitudinal Household Survey Data: Some Tests for Three Developing-Country Samples, Harold Alderman, Jere R. Behrman, Hans-Peter Kohler, John A. Maluccio, Susan Cotts Watkins, October 2000
97
Socioeconomic Differentials in Child Stunting Are Consistently Larger in Urban Than in Rural Areas, Purnima Menon, Marie T. Ruel, and Saul S. Morris, December 2000
98
Participation and Poverty Reduction: Issues, Theory, and New Evidence from South Africa, John Hoddinott, Michelle Adato, Tim Besley, and Lawrence Haddad, January 2001
99
Cash Transfer Programs with Income Multipliers: PROCAMPO in Mexico, Elisabeth Sadoulet, Alain de Janvry, and Benjamin Davis, January 2001
100
On the Targeting and Redistributive Efficiencies of Alternative Transfer Instruments, David Coady and Emmanuel Skoufias, March 2001
101
Poverty, Inequality, and Spillover in Mexico’s Education, Health, and Nutrition Program, Sudhanshu Handa, Mari-Carmen Huerta, Raul Perez, and Beatriz Straffon, March 2001
102
School Subsidies for the Poor: Evaluating a Mexican Strategy for Reducing Poverty, T. Paul Schultz, March 2001
103
Targeting the Poor in Mexico: An Evaluation of the Selection of Households for PROGRESA, Emmanuel Skoufias, Benjamin Davis, and Sergio de la Vega, March 2001
104
An Evaluation of the Impact of PROGRESA on Preschool Child Height, Jere R. Behrman and John Hoddinott, March 2001
105
The Nutritional Transition and Diet-Related Chronic Diseases in Asia: Implications for Prevention, Barry M. Popkin, Sue Horton, and Soowon Kim, March 2001
106
Strengthening Capacity to Improve Nutrition, Stuart Gillespie, March 2001
107
Rapid Assessments in Urban Areas: Lessons from Bangladesh and Tanzania, James L. Garrett and Jeanne Downen, April 2001
108
How Efficiently Do Employment Programs Transfer Benefits to the Poor? Evidence from South Africa, Lawrence Haddad and Michelle Adato, April 2001
109
Does Cash Crop Adoption Detract From Childcare Provision? Evidence From Rural Nepal, Michael J. Paolisso, Kelly Hallman, Lawrence Haddad, and Shibesh Regmi, April 2001
110
Evaluating Transfer Programs Within a General Equilibrium Framework, Dave Coady and Rebecca Lee Harris, June 2001
111
An Operational Tool for Evaluating Poverty Outreach of Development Policies and Projects, Manfred Zeller, Manohar Sharma, Carla Henry, and Cécile Lapenu, June 2001
112
Effective Food and Nutrition Policy Responses to HIV/AIDS: What We Know and What We Need to Know, Lawrence Haddad and Stuart Gillespie, June 2001
113
Measuring Power, Elizabeth Frankenberg and Duncan Thomas, June 2001
